Linking physical locations and online channels in a database

ABSTRACT

In some implementations, a device may receive, from one or more data sources, information indicating a plurality of data sets, where the plurality of data sets indicate information associated with respective physical locations or online locations. The device may identify a data set, from the plurality of data sets, that indicates information associated with an online location, where the information includes at least one of an entity name, an address, a phone number, a uniform resource locator, an entity identifier, or metadata. The device may parse the data set to identify information for a set of features. The device may analyze the information for the set of features to determine a brand associated with the online location. The device may pair the online location with the brand in the database such that the online location is linked with a first physical location of the brand in the database.

BACKGROUND

Data storage, such as a database, a table, and/or a linked list, refersto a set of related data and the way it is organized. A relationaldatabase is a collection of schemas, tables, queries, reports, or views.A data storage management system is an application that interacts withusers, other applications, and databases to allow definition, creation,querying, updating, and/or administration of data storage.

SUMMARY

In some implementations, a system for linking physical location data andonline channel data of entities in a database includes one or morememories; and one or more processors, communicatively coupled to the oneor more memories, configured to: receive information associated with aplurality of locations, where the plurality of locations include atleast one in-person location and at least one online location; identify,from the plurality of locations, candidate locations, parse informationassociated with the candidate locations to identify information for aset of features, where the set of features include at least one of aname, a phone number, a geographic location, a uniform resource locator,a headquarter address, an entity category, metadata, or transactiondata; apply a machine learning model to the information for the set offeatures for the candidate locations; determine whether the candidatelocations are associated with a same entity based on an output of themachine learning model; and add the candidate locations to a graphassociated with the entity if the candidate locations are associatedwith the entity, where the graph associated with the entity indicates atleast one online location associated with the entity and at least onein-person location associated with the entity.

In some implementations, a method of linking physical locations andonline locations with a brand in a database includes receiving, by adevice and from one or more data sources, information indicating aplurality of data sets, where the plurality of data sets indicateinformation associated with respective physical locations or onlinelocations; identifying, by the device, a data set, from the plurality ofdata sets, that indicates information associated with an onlinelocation, where the information includes at least one of an entity name,an address, a phone number, a uniform resource locator, an entityidentifier, or metadata; parsing, by the device, the data set toidentify information for a set of features; analyzing, by the device,the information for the set of features to determine a brand associatedwith the online location; and pairing, by the device, the onlinelocation with the brand in the database such that the online location islinked with a first physical location of the brand in the database.

In some implementations, a non-transitory computer-readable mediumstoring a set of instructions includes one or more instructions that,when executed by one or more processors of a device, cause the deviceto: receive information associated with physical locations of entitiesand online channels of entities, where the information includes aplurality of data sets, where each data set, of the plurality of datasets, indicates information associated with a physical location or anonline channel; process the information based on one or more features toidentify candidate data sets, where the candidate data sets include twoor more data sets with at least one data set associated with a physicallocation and at least one data set associated with an online channel;apply a model to the candidate data sets to determine a score, where thescore indicates a likelihood that the two or more data sets areassociated with a same entity; and link the two or more data sets in thedatabase based on a determination that the score satisfies a threshold,where the linking indicates that the two or more data sets areassociated with the same entity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C are diagrams of an example implementation relating tolinking physical location data and online channel data in a database.

FIG. 2 is a diagram illustrating an example of training and using amachine learning model in connection with linking physical location dataand online channel data in a database.

FIG. 3 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 4 is a diagram of example components of one or more devices of FIG.3 .

FIG. 5 is a flowchart of an example process relating to linking physicallocation data and online channel data in a database.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

An entity, such as a merchant or other company, may be associated withan online presence (e.g., an online channel, such as a website) as wellas one or more physical locations (e.g., brick-and-mortar locations). Inan ontology of the entity, a user may associate the online presence andthe physical locations of the entity with a brand of the entity. A brandmay be a name, term, design, symbol or any other feature that identifiesone entity's good or service as distinct from those of other entities.To gain an understanding of different entities, a database may becreated that stores location information associated with differententities. For example, a database may be created that links information(e.g., name, geographic location, address, and/or phone number) of allphysical locations of an entity. Similarly, a database may be createdthat stores information (e.g., name, uniform resource locator (URL),and/or website directory) of different online channels (e.g., websites)and links each online channel with an entity.

However, it is difficult to associate or link the online channels of anentity with the physical locations of the entity. For example, an onlinechannel may be associated with different types of location information(e.g., a URL, a headquarter address, and/or a headquarter phone number)than location information associated with a physical location (e.g., ageographic location, a local address, and/or a local phone number).Therefore, it is difficult to determine when an online channel and aphysical location are associated with the same entity (e.g., the samebrand). Moreover, an online channel may be associated with multiplephysical locations, each of which may have different locationinformation. Consequently, it is difficult to determine when an onlinechannel and the multiple physical locations are associated with the sameentity. As a result, to gain an understanding of a brand (e.g., acrossall physical locations and online channels associated with an entity),it is necessary to identify, create, and/or parse through multipledatabases and/or multiple data sources to collect information associatedwith the brand. This consumes significant computing resources (e.g.,processing resources), network resources, and time resources associatedwith identifying, creating, calling, and/or parsing through multipledatabases and/or multiple data sources to collect information associatedwith the brand. Additionally, a database that includes locationinformation for different entities may include hundreds, thousands, ormillions of entries. Therefore, identifying, creating, and/or storingthe multiple databases and/or multiple data sources needed to collectinformation associated with the brand consumes significant memoryresources.

Some implementations described herein enable linking physical locationdata of an entity and online channel data of the entity in a databaseunder a brand. For example, a system may collect location informationassociated with physical locations and location information associatedwith online channels. The system may process or analyze the informationto identify a brand associated with a physical location and/or an onlinechannel. In some implementations, the system may process or analyze theinformation to determine whether an online channel and a physicallocation are associated with the same entity or brand. The system maylink information associated with online channel(s) and/or informationassociated with physical location(s) that are associated with the samebrand. For example, the system may create or update a graph (e.g., acomponent graph or a knowledge graph) that links identifiers or data ofonline channel(s) and physical location(s) that are associated with thesame brand. The system may store the linked information associated withonline channel(s) and/or information associated with physicallocation(s) that are associated with the same brand in a database suchthat the information associated with online channel(s) and/orinformation associated with physical location(s) can be quickly andeasily identified in a single database.

As a result, the system may be enabled to analyze information associatedwith a brand in a more efficient manner. For example, the system mayreceive a request from a third party that requests or is based oninformation associated with a brand. The system may be enabled toquickly and easily identify information associated with each onlinechannel and/or information associated with each physical location of thebrand by parsing or searching the database for the brand, as compared toparsing or searching multiple databases with potentially differentsearch queries or application programming interface (API) calls.Moreover, the system may be enabled to aggregate and/or analyze dataassociated with the brand more efficiently (e.g., as compared to usingmultiple, disparate databases) because the system can quickly identifyand/or retrieve data across each online channel and/or each physicallocation of the brand using the database (e.g., a single database). Thisenables the system to process a request for data associated with a brandand/or aggregate or analyze data associated with the brand faster. As aresult, the system may conserve significant computing resources (e.g.,processing resources and memory resources) and/or network resources thatwould have otherwise been used by the system to identify, create, call,and/or parse through multiple databases and/or multiple data sources tocollect information associated with the brand.

Additionally, because the system is enabled to organize and/or storeinformation by brand across each online channel and each physicallocation of the brand, the system may be enabled to eliminateduplicative information associated with the brand, such as a phonenumber and/or a headquarter address, among other examples. Because thedatabase may include hundreds, thousands, or millions of entries,enabling the system to eliminate duplicative information associated witha brand by linking, in the database, information associated with eachonline channel and information associated with each physical location ofthe brand may conserve significant memory resources. Conserving memoryresources in this manner enables the system to increase processingefficiency and/or reduce a processing time associated with processing arequest for data associated with a brand and/or aggregating or analyzingdata associated with the brand.

FIGS. 1A-1C are diagrams of an example 100 associated with linkingphysical location data and online channel data in a database. As shownin FIGS. 1A-1C, example 100 includes a server device that communicatesand/or retrieves information from one or more data sources. The serverdevice may communicate with a client device to receive a request and/orprovide information associated with a brand, as described in more detailherein. These devices are described in more detail in connection withFIGS. 3 and 4 .

As shown in FIG. 1A, and by reference number 105, the server device mayreceive location information for physical locations and online channelsfrom one or more data sources. An online channel may include a website,a webpage, a group page, and/or a page or account associated with aplatform, such as a social media platform or an exchange platform (e.g.,a Facebook page or an Amazon seller account). The location informationmay include information that can be used to locate and/or identify thephysical location and/or online channel. For example, for a physicallocation, location information may include an entity name, an address, aphone number, a geographic location (e.g., longitude and latitudecoordinates), and/or a category (e.g., an entity category or a sellercategory), among other examples. For an online channel, locationinformation may include a uniform resource locator (URL), a networkaddress, and/or information that is extracted from the online channel,among other examples. The information that is extracted from the onlinechannel may include an entity name, an address, a headquarter address, aphone number, a category (e.g., an entity category or a sellercategory), a directory, metadata, and/or location information for one ormore physical locations, among other examples.

In some implementations, the server device may obtain (or may instructanother device to obtain) location information via an automatedweb-based interaction (e.g., web crawling, web scraping, data mining,web searching, and/or database searching). For example, the serverdevice may obtain (or may instruct another device to obtain) locationinformation for online channels via the automated web-based interaction.The server device (or another device) may store the location informationobtained via the automated web-based interaction in a data sourceincluded in the one or more data sources.

As shown by reference number 110, the server device may obtain one ormore data sets (including location information) for multiple entities.An entry in a data set may indicate location information for a physicallocation or an online channel. For example, as shown in FIG. 1A, anentry may indicate location information for a physical location with aname of Store A, an address of 123 Main Street, New York, and a phonenumber of 826-735-3815. Similarly, an entry may indicate locationinformation for an online channel with a phone number of 826-735-3815and a URL of www.StoreA.com.

In some implementations, the server device may arrange or organize thelocation information received from the one or more data sources toidentify a set of features associated with the location information. Theset of features may include an entity name, address, phone number, URL,geographic location, and/or category, among other examples. The serverdevice may arrange or organize the location information received fromthe one or more data sources such that the server device is enabled toidentify values or inputs for the set of features that is indicated bythe location information. For example, the server device may receiveunorganized and/or raw data from the one or more data sources. For aphysical location or an online channel, the server device may identifyvalues or inputs for the set of features. The server device may storethe identified values or inputs for the set of features in a datastructure, such as a database, in an organized manner (e.g., by feature,as shown in FIG. 1A), such that the server device is enabled to easilycompare information between different physical locations or onlinechannels for a certain feature.

In some implementations, the server device may obtain multiple datasets. The server device may arrange and/or organize the multiple datasets such that the multiple data sets are structured in a similarmanner. For example, the server device may arrange the multiple datasets by feature, such that the server device is enabled to compare anentry for a feature of a physical location or online channel in a firstdata set to an entry for the same feature of a physical location oronline channel in a second data set.

As shown by reference number 115, in some implementations, the serverdevice may process (or pre-process) the location information to identifyone or more candidate locations. Candidate locations (or a candidatepair) may be two or more locations (e.g., physical locations and/oronline channels) that are potentially associated with the same entity(e.g., that are candidates for being associated with the same entity).In some implementations, the server device may process or analyze thelocation information to identify candidate locations that have athreshold likelihood of being associated with the same entity. Theserver device may analyze information for a certain feature across allentries included in a data set (or may compare information for a certainfeature between entries of multiple data sets) to identify the candidatelocations. For example, the server device may analyze the informationfor a feature (or a subset of features from the set of features) toidentify entries that have similar inputs or values for the feature. Theserver device may determine the similarity based on one or moresimilarity analysis techniques, such as a semantic similarity, a cosinesimilarity, a centroid similarity, and/or an exact match, among otherexamples. In some implementations, the server device may determine asimilarly score that indicates a likelihood that two or more locationsare associated with the same entity. The server device may determinethat two or more locations are candidate locations if a similarity scorefor the two or more locations satisfies a threshold. This processing orpre-processing enables the server device to filter or reduce an amountof information or entries that the server device is required to analyzeand/or process in actions described in more detail below. As a result,computing resources, memory resources, and/or time resources may beconserved by the server device that would have otherwise been used hadthis processing or pre-processing not been performed by the serverdevice.

As shown in FIG. 1B, and by reference number 120, the server device mayanalyze information associated with locations (e.g., candidate locationsor other locations) to determine a brand (or entity) associated with thelocations. For example, the server device may analyze locationinformation for a physical location or an online channel to determine abrand associated with the physical location or the online channel. Insome implementations, the server device may input the locationinformation into a model, such as a machine learning model. The machinelearning model may provide an output based on the input of the locationinformation. The server device may determine the brand associated withthe physical location or the online channel based on the output of themachine learning model. For example, the output of the machine learningmay indicate the brand. In some implementations, the output of themachine learning model may be a score that indicates a likelihood thatthe physical location or the online channel is associated with a certainbrand. The server device may determine that the physical location or theonline channel is associated with the brand if the score output by themachine learning model satisfies a threshold. Example machine learningtechniques used to determine the brand associated with the physicallocation or the online channel are described below in more detail inconnection with FIG. 2 .

In some implementations, the server device may determine whethercandidate locations (e.g., two or more locations) are associated withthe same brand. The server device may compare location informationacross the candidate locations. For example, the server device maycompare entity names, phone numbers, addresses, headquarter addresses,geographic locations, categories, metadata, and/or transactioninformation (e.g., transaction dates, amounts, locations, and/orcategories) across the candidate locations.

In some implementations, the server device may determine a similarityscore for a feature, across the candidate locations, that indicates alikelihood that the input or value for the feature is associated withthe same brand across the candidate locations. In some implementations,the similarity score for a feature may be a binary match (e.g., a valueof 1) or no match (e.g., a value of 0). In some implementations, thesimilarity score for a feature may a value between 0 and 1, where avalue closer to 1 indicates a higher likelihood that the input or valuefor the feature is associated with the same brand. For example, theserver device may determine a similarity score for an entity name (orURL) across the candidate locations. In some implementations, the serverdevice may compare an entity name of a physical location to a URL of anonline channel to determine a similarity score between the entity nameand the URL (e.g., between an entity name of Store B and a URL ofwww.ShopStoreB.com). In some implementations, the server device may usedifferent similarity analysis techniques for different features. Forexample, for a comparison between entity names and/or URLs, the serverdevice may use a string similarity technique, or a Jaro-Winklertechnique (e.g., a string metric measuring an edit distance between twosequences). For a comparison between phone numbers, the server devicemay use an exact match technique, where the score is either 1 (if thephone numbers match) or 0 (if the phone numbers do not match).

The server device may use the similarity scores for different featuresas inputs to a machine learning model. For example, the server devicemay input a similarity score for a name or URL comparison, an addresscomparison, and/or a phone number comparison, among other examples. Anoutput of the machine learning model may indicate a likelihood that thecandidate locations are associated with the same brand. For example, anoutput of the machine learning model may be a score that indicates thelikelihood that the candidate locations are associated with the samebrand. The server device may determine whether the score satisfies athreshold. If the score does satisfy the threshold, then the serverdevice may determine that the candidate locations are associated withthe same brand. If the score does not satisfy the threshold, then theserver device may determine that the candidate locations are notassociated with the same brand.

The techniques described above enable the server device to link onlinechannels and physical locations of a brand or entity. In someimplementations, the linking may be a one-to-many relationship. Forexample, an online channel (such as a website) of a brand may be linkedto multiple physical locations of the brand. Additionally, oralternatively, multiple online channels (e.g., multiple websites orpages) of a brand may be linked together. In some implementations, themultiple online channels may be linked to one or more physical locationsof the brand. This enables the server device to gain a betterunderstanding of an entire brand, by linking all commerce channels(e.g., online channels and physical locations) of the brand in a singlelocation (e.g., in a database, as described in more detail below).

As shown by reference number 125, the server device may create or updatea graph 130 (e.g., a component graph or a knowledge graph) that linksinformation associated with candidate locations that are associated withthe same brand. For example, the server device may create a graph havingnodes (e.g., components or elements) that correspond to an identifier ofa location (e.g., a physical location or an online channel). The serverdevice may link or connect nodes in the graph (e.g., may connect nodeswith an edge) if the corresponding locations are associated with thesame brand or entity.

For example, as described above, the server device may determine a brandassociated with a physical location or online channel and/or maydetermine that candidate locations are associated with the same brand.The server device may determine whether a graph associated with thebrand is already stored by the server device (e.g., in a database). Ifthe server device determines that a graph associated with the brand isalready stored by the server device, then the server device may identifyand/or retrieve the graph and may update the graph to include anidentifier associated with the location (e.g., the physical location orthe online channel) or the candidate locations. If the server devicedetermines that a graph associated with the brand is not stored by theserver device, then the server device may create a graph that includesan identifier associated with the location (e.g., the physical locationor the online channel) or the candidate locations and indicates thelinks or connection between the locations indicated by the graph.

In FIG. 1B, an example graph 130 is depicted. As shown by referencenumber 135, the graph 130 includes a first set of linked locationsassociated with a first brand. The first set of linked locations maylink an identifier of a first online channel (website A) to a location1, a location 2, and a location 3. For example, the server device maydetermine that website A and location 1, location 2, and location 3 areassociated with the same brand or entity (e.g., the first brand), asdescribed above. The server device may create or update the graph 130 toinclude links or connections between website A and location 1, location2, and location 3. As a result, the server device may quickly identifythat website A and location 1, location 2, and location 3 are associatedwith the same brand based on the linkages.

Similarly, as shown by reference number 140, the graph 130 includes asecond set of linked locations associated with a second brand. Thesecond set of linked locations may link an identifier of a second onlinechannel (website B) with a location 4 and a location 5. Additionally,the second set of linked locations may link an identifier of a thirdonline channel (website C) with location 4 and location 5. Althoughwebsite B and website C may not be directly linked in the graph 130, theserver device may be enabled to determine that website B and website Care associated with the same brand based on the corresponding linkagesto location 4 and location 5.

By using the linkages in a graph, as described above, the server devicemay be able to associate online channels and physical locations with abrand. For example, the server device may determine that each node inthe graph that includes common links or is otherwise linked together(e.g., directly or through another node) is associated with the samebrand. Graph 130 is provided as an example. In some implementations, agraph 130 may be brand-specific and may only include nodes (e.g.,corresponding to location identifiers) associated with a single brand.In some implementations, a graph 130 may include nodes associated with aplurality of brands (e.g., tens, hundreds, or thousands of brands).

As shown in FIG. 1C, and by reference number 145, the server device maystore linked brand location information in a database (e.g., a branddatabase). The brand location information may include locationinformation for each physical location and each online channel that theserver device has determined is associated with a same brand, asdescribed above. For example, as shown in FIG. 1C, the server device maystore brand location information for a brand Store A. The brand locationinformation may include location information for a physical location(e.g., with a name of Store A, an address of 123 Main Street, New York,and a phone number of 826-735-3815) and an online location (e.g., with aphone number of 826-735-3815 and a URL of www.StoreA.com).

Similarly, the server device may store brand location information for abrand Store B. The brand location information may include locationinformation for a first physical location (e.g., with a name of Store B,an address of 45 Central Ave, New York, and a phone number of826-276-1234), a second physical location (e.g., with a name of Store B,an address of 36 West Street, Pennsylvania, and a phone number of732-194-9375) and an online location (e.g., with a phone number of123-276-1234 and a URL of www.ShopStoreB.com). The server device maystore brand location information in the brand database for a brand suchthat the location information for each physical location and/or onlinechannel associated with the brand is linked to the brand (e.g., using anidentifier or flag in the brand database, shown in FIG. 1C as a brandfeature of “Store A” and “Store B” in the “Brand” column). The serverdevice may store brand location information in the brand database formultiple brands. In some implementations, the server device may store agraph (e.g., graph 130) for a brand in the brand database. In someimplementations, the server device may determine the brand locationinformation associated with a brand (e.g., that is to be stored in thebrand database as described above) based on a graph (e.g., graph 130)for the brand.

In some implementations, brand location information may includeduplicative information. For example, as shown in FIG. 1C, the brandlocation information for Store A includes the same phone number for thephysical location and the online channel associated with Store A brand.The server device may identify duplicative information included in thebrand location information. The server device may remove (e.g., deletefrom memory) or refrain from storing (e.g., in memory) the duplicativeinformation such that information included in the brand locationinformation is only stored once by the server device. In someimplementations, the server device may include a mapping to the locationwhere information is stored in the brand database. For example, for thebrand location information of Store A, rather than storing the samephone number twice, the server device may store the phone number onceand include a mapping to the location of the stored phone number insubsequent location information entries. As a result, storing the brandlocation information in this manner enables the server device to reducean amount of information stored by the server device in the branddatabase. As the brand database may include hundreds, thousands, ormillions of entries, enabling the server device to eliminate or notstore duplicative information included in brand location information inthe brand database may conserve significant memory resources. Conservingmemory resources in this manner enables the server device to increaseprocessing efficiency and/or reduce a processing time associated withprocessing a request for data associated with a brand and/or aggregatingor analyzing data associated with the brand, as described in more detailherein.

As shown by reference number 150, the server device may receive arequest, associated with a brand, from a client device. For example, theserver device may receive a request for transaction data associated witha brand. In some implementations, the server device may receive arequest associated with multiple brands. For example, the server devicemay receive a request associated with a category of goods or servicesthat is associated with multiple brands. The server device may determineor identify one or more brands associated with the category of goods orservices.

As shown by reference number 155, based on receiving the request, theserver device may identify information associated with the brand usingthe brand database. For example, the request may be for transaction dataassociated with a brand. The server device may identify each physicallocation and online channel associated with the brand by searching forthe brand in the brand database and/or identifying a graph (e.g., acomponent graph) associated with the brand. The server device mayaggregate transaction data across each physical location and onlinechannel associated with the brand. In this way, the server device isenabled to provide more accurate transaction data for a brand. Moreover,the server device may quickly identify each physical location and onlinechannel associated with the brand using the brand database, therebyreducing computing resources and/or processing time associated withidentifying the information associated with the request.

In some implementations, if the request is associated with multiplebrands or a category, the server device may identify the brandsassociated with the request and may identify each physical location andonline channel associated with the brands. The server device may beenabled to compare transaction data across the brands, identify specifictypes or categories of transactions across the brands, and/or identifymarketing or advertisement information (e.g., deals advertised by thebrands and/or prices of a specific product or service at each brand)across the brands, among other examples. Therefore, the server devicemay be enabled to quickly and easily compare information across multiplebrands while having a more accurate understanding of each brand becauseeach physical location and online channel associated with the brands canbe identified by the server device. In this way, the server device isenabled to provide more accurate information across multiple brands,conserving computing resources and processing time that would haveotherwise been used to identify each brand, collect location informationfor each brand (e.g., physical locations and online channels) usingmultiple databases and/or multiple data sources, collect information foreach brand, and/or compare or aggregate the information for each brand.

In some implementations, the request may be associated with an account(e.g., a transaction account or a credit account) of an entity that isusing the client device. For example, the request may be for the serverdevice to create a temporary credential or identifier for the account(e.g., a virtual identifier or a virtual card number) or a temporarytransaction card. The request may indicate a brand or entity with whichthe temporary identifier or temporary transaction card is to beassociated (e.g., if the temporary identifier or temporary transactioncard is used to initiate a transaction at an entity other than theindicated entity or brand, then the transaction will be declined). Theserver device may identify physical locations and online channelsassociated with the brand by searching for the brand in the branddatabase and/or identifying a graph (e.g., a component graph) associatedwith the brand.

In some implementations, the request may indicate a geographiclimitation in addition to the brand or entity limitation. For example,the request may indicate that the temporary identifier or temporarytransaction card is to be valid for online transactions and/or forin-person transactions that are within a certain city, a certain zipcode, and/or a certain state, among other examples. The server devicemay identify physical locations of the brand or entity that are locatedwithin the geographic limitation indicated by the request. The serverdevice may create the temporary identifier or temporary transaction cardsuch that the temporary identifier or temporary transaction card isenabled to complete transactions at each physical location and/or onlinechannel associated with the brand or entity identified by the serverdevice, as described above.

Therefore, the server device is enabled to determine physical locationsand/or online channels that are to be associated with the temporaryidentifier or temporary transaction card faster and more accurately,thereby reducing computing resources and/or processing time associatedwith creating the temporary identifier or temporary transaction card.Moreover, the server device is enabled to reduce a chance of a falsedecline associated with the temporary identifier or temporarytransaction card by having a complete understanding of all physicallocations and/or online channels of a brand. For example, the serverdevice may reduce a chance that a transaction initiated using thetemporary identifier or temporary transaction card is declined at aphysical location or online channel that is associated with the brand orentity and that did satisfy the request from the client device.

As shown by reference number 160, the server device may fulfill therequest received from the client device. For example, the server devicemay transmit an indication of transaction data associated with a brand.In some implementations, the server device may transmit an indication ofaggregated or comparative information across multiple brands. In someimplementations, the server device may transmit an indication of atemporary identifier. In some implementations, the server device maycause a temporary transaction card to be manufactured and may send thetemporary transaction card to an entity associated with the request. Asdescribed above, as the server device has a complete understanding of abrand (e.g., has linked physical locations and online channels of thebrand), the server device is enabled to quickly fulfill a requestassociated with the brand (or multiple brands) by eliminating the needto identify and/or determine each physical locations and online channelsof the brand. Moreover, the server device may provide more accurateinformation associated with the brand as the server device is enabled toaggregate information associated with the brand across all linkedphysical locations and online channels of the brand (e.g., rather thanrelying on information from a single online channel/physical location orinformation from a subset of online channels/physical locations).

In some implementations, the server device may perform one or moreactions associated with a brand without receiving a request from theclient device. For example, the server device may receive an indicationof a transaction completed using resources of an account associated witha user. The server device may determine or identify a physical locationor an online channel associated with the transaction. The server devicemay determine or identify a brand associated with the physical locationor the online channel associated with the transaction (e.g., using thebrand database). The server device may identify information associatedwith the brand that is to be provided to the user associated with thetransaction. For example, the server device may identify a logo or brandidentifier, a URL of an online channel associated with the brand (e.g.,if the transaction was completed at a physical location), an incentiveor reward program associated with the brand, and/or transaction dataassociated with the brand, among other examples. The server device maytransmit an indication of information associated with the brand to aclient device associated with the user. Therefore, the user is enabledto quickly identify the brand associated with the transaction or otherinformation associated with the brand (e.g., a return policy, terms andconditions, a website, and/or among other examples). This conservescomputing resources that would have otherwise been used by the user toidentify and/or retrieve the information associated with the brand.

In some implementations, the server device may aggregate and/or analyzeinformation across a brand (e.g., across physical locations and onlinechannels associated with the brand). In some implementations, the serverdevice may receive transaction data that indicates a physical locationor an online channel associated with the transaction data. The serverdevice may identify a brand (e.g., in the brand database) based on thephysical location or the online channel associated with the transactiondata. In some implementations, the server device may identify acomponent graph that indicates the physical location or the onlinechannel associated with the transaction data (e.g., and indicates abrand associated with the component graph). The server device mayassociate with transaction data with the brand that is associated withthe physical location or the online channel associated with thetransaction data. In this way, the server device may quickly and easilyaggregate transaction data across a brand (e.g., across physicallocations and online channels associated with the brand).

In some implementations, the server device may obtain transaction dataassociated with a brand across physical locations and online channelsassociated with the brand (e.g., identified using the brand databaseand/or component graph(s), as described above). The server device mayanalyze the transaction data to determine transaction trends or patternsassociated with the brand. Additionally, or alternatively, the serverdevice may analyze or compare online transaction data and in-persontransaction data (e.g., transactions completed at a physical location)for brand to identify trends or patterns across online transaction datacompared to in-person transaction data for the brand. As the serverdevice may be enabled to aggregate the information (e.g., transactiondata) associated with the brand across physical locations and onlinechannels, the server device is enabled to quickly and more efficientlyanalyze and/or compare the information associated with the brand. Thisconserves computing resources (e.g., processing resources) and timeresources that would have otherwise been used to analyze and/or comparethe information associated with the brand.

As indicated above, FIGS. 1A-1C are provided as an example. Otherexamples may differ from what is described with regard to FIGS. 1A-1C.

FIG. 2 is a diagram illustrating an example 200 of training and using amachine learning model in connection with linking physical location dataand online channel data in a database. The machine learning modeltraining and usage described herein may be performed using a machinelearning system. The machine learning system may include or may beincluded in a computing device, a server, a cloud computing environment,or the like, such as the server device described in more detailelsewhere herein.

As shown by reference number 205, a machine learning model may betrained using a set of observations. The set of observations may beobtained from training data (e.g., historical data), such as datagathered during one or more processes described herein. In someimplementations, the machine learning system may receive the set ofobservations (e.g., as input) from one or more data sources or a serverdevice, as described elsewhere herein.

As shown by reference number 210, the set of observations includes afeature set. The feature set may include a set of variables, and avariable may be referred to as a feature. A specific observation mayinclude a set of variable values (or feature values) corresponding tothe set of variables. In some implementations, the machine learningsystem may determine variables for a set of observations and/or variablevalues for a specific observation based on input received from one ormore data sources or a server device. For example, the machine learningsystem may identify a feature set (e.g., one or more features and/orfeature values) by extracting the feature set from structured data, byperforming natural language processing to extract the feature set fromunstructured data, and/or by receiving input from an operator.

As an example, a feature set for a set of observations may include afirst feature of entity name or URL, a second feature of geographiclocation (e.g., address) or channel, a third feature of phone number,and so on. As shown, for a first observation, the first feature may havea value of Store A, the second feature may have a value of 123 MainStreet, New York, the third feature may have a value of 826-735-3815,and so on. These features and feature values are provided as examples,and may differ in other examples. For example, the feature set mayinclude one or more of the following features: a headquarters' address,a headquarters' phone number, an entity category, metadata, ortransaction data (e.g., transaction amount, average transaction amount,transaction dates or times, and/or transaction locations), among otherexamples.

As shown by reference number 215, the set of observations may beassociated with a target variable. The target variable may represent avariable having a numeric value, may represent a variable having anumeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiples classes, classifications, orlabels) and/or may represent a variable having a Boolean value. A targetvariable may be associated with a target variable value, and a targetvariable value may be specific to an observation. In example 200, thetarget variable is a brand, which has a value of Store A for the firstobservation.

The feature set and target variable described above are provided asexamples, and other examples may differ from what is described above.For example, for a target variable that indicates whether two or morecandidate locations are associated with the same brand, the feature setmay include similar (or the same) features as described above. However,the values of the features may be scores indicating a similarity betweenthe two or more candidate locations for each feature. The value of thetarget variable may be 1 (if the two or more candidate locations areassociated with the same brand) or 0 (if the two or more candidatelocations are not associated with the same brand).

The target variable may represent a value that a machine learning modelis being trained to predict, and the feature set may represent thevariables that are input to a trained machine learning model to predicta value for the target variable. The set of observations may includetarget variable values so that the machine learning model can be trainedto recognize patterns in the feature set that lead to a target variablevalue. A machine learning model that is trained to predict a targetvariable value may be referred to as a supervised learning model.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable. This may bereferred to as an unsupervised learning model. In this case, the machinelearning model may learn patterns from the set of observations withoutlabeling or supervision, and may provide output that indicates suchpatterns, such as by using clustering and/or association to identifyrelated groups of items within the set of observations.

As shown by reference number 220, the machine learning system may traina machine learning model using the set of observations and using one ormore machine learning algorithms, such as a regression algorithm, adecision tree algorithm, a neural network algorithm, a k-nearestneighbor algorithm, a support vector machine algorithm, or the like.After training, the machine learning system may store the machinelearning model as a trained machine learning model 225 to be used toanalyze new observations.

As shown by reference number 230, the machine learning system may applythe trained machine learning model 225 to a new observation, such as byreceiving a new observation and inputting the new observation to thetrained machine learning model 225. As shown, the new observation mayinclude a first feature of entity name or URL, a second feature ofgeographic location (e.g., address) or channel, a third feature of phonenumber, and so on, as an example. The machine learning system may applythe trained machine learning model 225 to the new observation togenerate an output (e.g., a result). The type of output may depend onthe type of machine learning model and/or the type of machine learningtask being performed. For example, the output may include a predictedvalue of a target variable, such as when supervised learning isemployed. Additionally, or alternatively, the output may includeinformation that identifies a cluster to which the new observationbelongs and/or information that indicates a degree of similarity betweenthe new observation and one or more other observations, such as whenunsupervised learning is employed.

As an example, the trained machine learning model 225 may predict avalue of Store A for the target variable of brand for the newobservation, as shown by reference number 235. Based on this prediction,the machine learning system may provide a first recommendation, mayprovide output for determination of a first recommendation, may performa first automated action, and/or may cause a first automated action tobe performed (e.g., by instructing another device to perform theautomated action), among other examples. The first recommendation mayinclude, for example, that a physical location or online channel isassociated with Store A. The first automated action may include, forexample, storing location information for the physical location or theonline channel in a database such that the location information isassociated with Store A or adding an identifier associated with thephysical location or the online channel to a component graph associatedwith Store A.

As another example, if the machine learning system were to predict avalue of 0 for a target variable that indicates whether two or morecandidate locations are associated with the same brand, then the machinelearning system may provide a second (e.g., different) recommendation(e.g., that candidate locations associated with the input values of thefeatures are not associated with the same brand) and/or may perform (orrefrain from performing) or cause performance of (or prevent performanceof) a second (e.g., different) automated action (e.g., refrain fromstoring location information associated with the candidate locations inthe database as linked with the same brand).

In some implementations, the trained machine learning model 225 mayclassify (e.g., cluster) the new observation in a cluster, as shown byreference number 240. The observations within a cluster may have athreshold degree of similarity. As an example, if the machine learningsystem classifies the new observation in a first cluster (e.g., acluster of physical locations or online channels associated with StoreA), then the machine learning system may provide a first recommendation,such as the first recommendation described above. Additionally, oralternatively, the machine learning system may perform a first automatedaction and/or may cause a first automated action to be performed (e.g.,by instructing another device to perform the automated action) based onclassifying the new observation in the first cluster, such as the firstautomated action described above.

As another example, if the machine learning system were to classify thenew observation in a second cluster (e.g., a cluster of physicallocations or online channels associated with Store B), then the machinelearning system may provide a second (e.g., different) recommendation(e.g., link the location information with store B) and/or may perform orcause performance of a second (e.g., different) automated action, suchas storing location information for the physical location or the onlinechannel in a database such that the location information is associatedwith Store B or adding an identifier associated with the physicallocation or the online channel to a component graph associated withStore B.

In some implementations, the recommendation and/or the automated actionassociated with the new observation may be based on a target variablevalue having a particular label (e.g., classification orcategorization), may be based on whether a target variable valuesatisfies one or more threshold (e.g., whether the target variable valueis greater than a threshold, is less than a threshold, is equal to athreshold, falls within a range of threshold values, or the like),and/or may be based on a cluster in which the new observation isclassified.

In this way, the machine learning system may apply a rigorous andautomated process to link information for physical locations and onlinelocations of a brand or entity. The machine learning system enablesrecognition and/or identification of tens, hundreds, thousands, ormillions of features and/or feature values for tens, hundreds,thousands, or millions of observations, thereby increasing accuracy andconsistency and reducing delay associated with linking information forphysical locations and online locations of a brand or entity relative torequiring computing resources to be allocated for tens, hundreds, orthousands of operators to manually link information for physicallocations and online locations of a brand or entity using the featuresor feature values.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2 .

FIG. 3 is a diagram of an example environment 300 in which systemsand/or methods described herein may be implemented. As shown in FIG. 3 ,environment 300 may include a server device 310, one or more datasources 320, a client device 330, and a network 340. Devices ofenvironment 300 may interconnect via wired connections, wirelessconnections, or a combination of wired and wireless connections.

The server device 310 includes one or more devices capable of receiving,generating, storing, processing, providing, and/or routing informationassociated with linking physical location data and online channel datain a database, as described elsewhere herein. The server device 310 mayinclude a communication device and/or a computing device. For example,the server device 310 may include a server, such as an applicationserver, a client server, a web server, a database server, a host server,a proxy server, a virtual server (e.g., executing on computinghardware), or a server in a cloud computing system. In someimplementations, the server device 310 includes computing hardware usedin a cloud computing environment.

The data source 320 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith linking physical location data and online channel data in adatabase, as described elsewhere herein. The data source 320 may includea communication device and/or a computing device. For example, the datasource 320 may include a database, a server, a database server, anapplication server, a client server, a web server, a host server, aproxy server, a virtual server (e.g., executing on computing hardware),a server in a cloud computing system, a device that includes computinghardware used in a cloud computing environment, or a similar type ofdevice. The data source 320 may communicate with one or more otherdevices of environment 300, as described elsewhere herein.

The client device 330 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith linking physical location data and online channel data in adatabase, as described elsewhere herein. The client device 330 mayinclude a communication device and/or a computing device. For example,the client device 330 may include a wireless communication device, amobile phone, a user equipment, a laptop computer, a tablet computer, adesktop computer, a wearable communication device (e.g., a smartwristwatch, a pair of smart eyeglasses, a head mounted display, or avirtual reality headset), or a similar type of device.

The network 340 includes one or more wired and/or wireless networks. Forexample, the network 340 may include a wireless wide area network (e.g.,a cellular network or a public land mobile network), a local areanetwork (e.g., a wired local area network or a wireless local areanetwork (WLAN), such as a Wi-Fi network), a personal area network (e.g.,a Bluetooth network), a near-field communication network, a telephonenetwork, a private network, the Internet, and/or a combination of theseor other types of networks. The network 340 enables communication amongthe devices of environment 300.

The number and arrangement of devices and networks shown in FIG. 3 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 3 . Furthermore, two or more devices shown in FIG. 3 maybe implemented within a single device, or a single device shown in FIG.3 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 300 may perform one or more functions described as beingperformed by another set of devices of environment 300.

FIG. 4 is a diagram of example components of a device 400, which maycorrespond to server device 310, data source 320, and/or client device330. In some implementations, server device 310, data source 320, and/orclient device 330 may include one or more devices 400 and/or one or morecomponents of device 400. As shown in FIG. 4 , device 400 may include abus 410, a processor 420, a memory 430, a storage component 440, aninput component 450, an output component 460, and a communicationcomponent 470.

Bus 410 includes a component that enables wired and/or wirelesscommunication among the components of device 400. Processor 420 includesa central processing unit, a graphics processing unit, a microprocessor,a controller, a microcontroller, a digital signal processor, afield-programmable gate array, an application-specific integratedcircuit, and/or another type of processing component. Processor 420 isimplemented in hardware, firmware, or a combination of hardware andsoftware. In some implementations, processor 420 includes one or moreprocessors capable of being programmed to perform a function. Memory 430includes a random access memory, a read only memory, and/or another typeof memory (e.g., a flash memory, a magnetic memory, and/or an opticalmemory).

Storage component 440 stores information and/or software related to theoperation of device 400. For example, storage component 440 may includea hard disk drive, a magnetic disk drive, an optical disk drive, a solidstate disk drive, a compact disc, a digital versatile disc, and/oranother type of non-transitory computer-readable medium. Input component450 enables device 400 to receive input, such as user input and/orsensed inputs. For example, input component 450 may include a touchscreen, a keyboard, a keypad, a mouse, a button, a microphone, a switch,a sensor, a global positioning system component, an accelerometer, agyroscope, and/or an actuator. Output component 460 enables device 400to provide output, such as via a display, a speaker, and/or one or morelight-emitting diodes. Communication component 470 enables device 400 tocommunicate with other devices, such as via a wired connection and/or awireless connection. For example, communication component 470 mayinclude a receiver, a transmitter, a transceiver, a modem, a networkinterface card, and/or an antenna.

Device 400 may perform one or more processes described herein. Forexample, a non-transitory computer-readable medium (e.g., memory 430and/or storage component 440) may store a set of instructions (e.g., oneor more instructions, code, software code, and/or program code) forexecution by processor 420. Processor 420 may execute the set ofinstructions to perform one or more processes described herein. In someimplementations, execution of the set of instructions, by one or moreprocessors 420, causes the one or more processors 420 and/or the device400 to perform one or more processes described herein. In someimplementations, hardwired circuitry may be used instead of or incombination with the instructions to perform one or more processesdescribed herein. Thus, implementations described herein are not limitedto any specific combination of hardware circuitry and software.

The number and arrangement of components shown in FIG. 4 are provided asan example. Device 400 may include additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 4 . Additionally, or alternatively, a set ofcomponents (e.g., one or more components) of device 400 may perform oneor more functions described as being performed by another set ofcomponents of device 400.

FIG. 5 is a flowchart of an example process 500 associated with linkingphysical location data and online channel data in a database. In someimplementations, one or more process blocks of FIG. 5 may be performedby a device (e.g., server device 310). In some implementations, one ormore process blocks of FIG. 5 may be performed by another device or agroup of devices separate from or including the device, such as datasource 320 and/or client device 330. Additionally, or alternatively, oneor more process blocks of FIG. 5 may be performed by one or morecomponents of device 400, such as processor 420, memory 430, storagecomponent 440, input component 450, output component 460, and/orcommunication component 470.

As shown in FIG. 5 , process 500 may include receiving, from one or moredata sources, information indicating a plurality of data sets (block510). In some implementations, the plurality of data sets indicateinformation associated with respective physical locations or onlinelocations. As further shown in FIG. 5 , process 500 may includeidentifying a data set, from the plurality of data sets, that indicatesinformation associated with an online location (block 520). In someimplementations, the information includes at least one of an entityname, an address, a phone number, a uniform resource locator, an entityidentifier, or metadata. As further shown in FIG. 5 , process 500 mayinclude parsing the data set to identify information for a set offeatures (block 530). As further shown in FIG. 5 , process 500 mayinclude analyzing the information for the set of features to determine abrand associated with the online location (block 540). As further shownin FIG. 5 , process 500 may include pairing the online location with thebrand in the database such that the online location is linked with afirst physical location of the brand in the database (block 550).

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise forms disclosed. Modifications may be made in light of the abovedisclosure or may be acquired from practice of the implementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software. Itwill be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be used to implement the systems and/or methods based on thedescription herein.

As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, not equal to the threshold, or thelike.

Although particular combinations of features are recited in the claimsand/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set. As used herein, aphrase referring to “at least one of” a list of items refers to anycombination of those items, including single members. As an example, “atleast one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c,and a-b-c, as well as any combination with multiple of the same item.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, or a combination of related and unrelateditems), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

What is claimed is:
 1. A system for linking physical location data andonline channel data of entities in a database, the system comprising:one or more memories; and one or more processors, communicativelycoupled to the one or more memories, configured to: receive informationassociated with a plurality of locations, wherein the plurality oflocations include at least one in-person location and at least oneonline location; identify, from the plurality of locations, candidatelocations; parse information associated with the candidate locations toidentify information for a set of features for the candidate locations,wherein the set of features includes at least one of a name, a phonenumber, a geographic location, a uniform resource locator, aheadquarters address, an entity category, metadata, or transaction data,and wherein parsing the information comprises: identifying the set offeatures by obtaining the set of features from structured data byperforming natural language processing to extract the set of featuresfrom unstructured data; iteratively training a machine training model,comprising at least one of a neural network or a support vector, togenerate a trained machine learning model, wherein the trained machinelearning model is trained using the set of features; apply the trainedmachine learning model to the information for the set of features forthe candidate locations; determine, based on applying the trainedmachine learning model, that a score associated with an output of thetrained machine learning model satisfies a threshold, wherein the scoreindicates a likelihood that the candidate locations are associated witha same entity; determine, based on the score, whether the candidatelocations are associated with the entity; and add information associatedwith the candidate locations to a graph associated with the entity ifthe candidate locations are associated with the same entity; receive arequest to generate one or more temporary credentials associated withthe entity; identify, based on the graph, at least one online locationand at least one physical location associated with the entity; andgenerate, based on identifying the at least one online location and theat least one physical location associated with the entity, the one ormore temporary credentials, wherein the one or more temporarycredentials are enabled to complete one or more transactions at the atleast one online location and the at least one physical location.
 2. Thesystem of claim 1, wherein the one or more processors are furtherconfigured to: receive, from a client device, a request for informationassociated with the entity; retrieve, from a database, the graphassociated with the entity; identify the information associated with theentity based on the graph; and provide, to the client device, theinformation associated with the entity.
 3. The system of claim 2,wherein the information associated with the entity includes at least oneof: in-person locations and online locations of the entity, ortransaction data associated with the entity.
 4. The system of claim 1,wherein the one or more processors are further configured to: receivetransaction data that indicates the at least one in-person location orthe at least one online location associated with a transaction; identifythat the at least one in-person location or the at least one onlinelocation associated with the transaction are indicated in the graphassociated with the entity; and associate the transaction with theentity and the at least one in-person location or the at least oneonline location.
 5. The system of claim 1, wherein the graph includesnodes that correspond to information of one or more of the at least oneonline location or the at least one physical location, and wherein oneor more nodes, of the nodes, that are associated with the same entityare connected via links.
 6. The system of claim 1, wherein the one ormore processors are further configured to: provide the one or moretemporary credentials.
 7. A method of linking physical locations andonline locations with a brand in a database, comprising: receiving, by adevice and from one or more data sources, information indicating aplurality of data sets, wherein the plurality of data sets indicateinformation associated with respective physical locations or onlinelocations; identifying, by the device, a data set, from the plurality ofdata sets, that indicates information associated with an onlinelocation, wherein the information includes at least one of an entityname, an address, a phone number, a uniform resource locator, an entityidentifier, or metadata; parsing, by the device, the data set toidentify information for a set of features, wherein parsing theinformation comprises: identifying the set of features by obtaining theset of features from structured data by performing natural languageprocessing to extract the set of features from unstructured data;iteratively training a machine training model, comprising at least oneof a neural network or a support vector, to generate a trained machinelearning model, wherein the trained machine learning model is trainedusing the set of features; determining, by the device and based onapplying the trained machine learning model to the information for theset of features, that a score associated with an output of the trainedmachine learning model satisfies a threshold, wherein the scoreindicates a likelihood that the online location is associated with abrand; determining, based on the score, the brand associated with theonline location; pairing, by the device, the online location with thebrand in the database such that the online location is linked with afirst physical location of the brand in the database; and addinginformation associated with the online location to a graph associatedwith the brand, wherein the graph provides link information associatedwith at least one online location and at least one physical locationrelated to the brand, and wherein the graph comprises links indicatingthat the at least one online location and at least one physical locationare associated with the same brand; receiving a request to generate oneor more temporary credentials associated with the brand; identifying,based on the graph, the at least one online location and the at leastone physical location associated with the brand; and generating based onidentifying the at least one online location and the at least onephysical location associated with the brand, the one or more temporarycredentials, wherein the one or more temporary credentials are enabledto complete one or more transactions at the at least one online locationand the at least one physical location.
 8. The method of claim 7,further comprising: creating a subset of the graph, wherein the graph isassociated with the brand, and wherein the subset of the graph links theat least one physical location of the brand with the at least one onlinelocation of the brand.
 9. The method of claim 7, further comprising:identifying a different data set, from the plurality of data sets, thatindicates information associated with a second physical location;parsing the different data set to identify information for the set offeatures; determining, based on analyzing the information for the set offeatures, that the second physical location is associated with thebrand; and pairing, in the database, the second physical location withthe brand such that the second physical location is linked with theonline location and the first physical location.
 10. The method of claim7, further comprising: comparing the plurality of data sets; determiningthat the one or more data sets, of the plurality of data sets, have alikelihood of being associated with the brand based on comparing theplurality of data sets; and identifying the one or more data sets basedon determining that the one or more data sets have a likelihood of beingassociated with the brand.
 11. The method of claim 7, furthercomprising: receiving an indication of a transaction completed at anentity location using resources of an account; identifying, in thedatabase, a brand associated with the entity location; and providing, toa client device associated with the account, information associated withthe brand, wherein the information associated with the brand includes atleast one of: a logo or brand identifier, a uniform resource locator ofan online location associated with the brand, an incentive or rewardprogram associated with the brand, or transaction data associated withthe brand.
 12. The method of claim 7, further comprising: obtaining,from the database, transaction data associated with the brand, whereinthe transaction data includes transaction data associated with eachphysical location and each online location linked with the brand in thedatabase.
 13. The method of claim 7, wherein the graph includes nodesthat correspond to information of at least one of the at least oneonline location or the at least one physical location, and wherein oneor more nodes, of the nodes, that are associated with the same entityare connected via links.
 14. The method of claim 7, wherein the machinelearning model is trained with information associated with a set offeatures for one or more locations associated with one or more brands,including the brand.
 15. The method of claim 7, further comprising:providing the one or more temporary credentials.
 16. A non-transitorycomputer-readable medium storing a set of instructions, the set ofinstructions comprising: one or more instructions that, when executed byone or more processors of a device, cause the device to: receiveinformation associated with physical locations of entities and onlinechannels of entities, wherein the information includes a plurality ofdata sets, and wherein each data set, of the plurality of data sets,indicates information associated with a physical location or an onlinechannel; process the information based on one or more features toidentify candidate data sets, wherein the candidate data sets includetwo or more data sets, of the plurality of data sets, with at least onedata set associated with a physical location and at least one data setassociated with an online channel, and wherein processing theinformation comprises: identifying the candidate data sets by obtainingthe candidate data sets from structured data by performing naturallanguage processing to extract the candidate data sets from unstructureddata; iteratively training a machine training model, comprising at leastone of a neural network or a support vector, to generate a trainedmachine learning model, wherein the trained machine learning model istrained using the candidate data sets; apply the trained machinelearning model to the candidate data sets to determine a score, whereinthe score indicates a likelihood that the two or more data sets areassociated with a same entity; link the two or more data sets in adatabase based on a determination that the score satisfies a threshold,wherein the linking indicates that the two or more data sets areassociated with the same entity; and add information associated with thetwo or more data sets to a graph associated with the same entity;receive a request to generate one or more temporary credentialsassociated with the entity; identify, based on the graph, at least oneonline location and at least one physical location associated with theentity; and generate, based on identifying the at least one onlinelocation and the at least one physical location associated with theentity, the one or more temporary credentials, wherein the one or moretemporary credentials are enabled to complete one or more transactionsat the at least one online location and the at least one physicallocation.
 17. The non-transitory computer-readable medium of claim 15,wherein the one or more instructions, when executed by the one or moreprocessors, further cause the device to: create a subset of the graph,associated with the entity, that indicates physical locations or onlinechannels associated with the two or more data sets and that links thephysical locations and the online channels associated with the entity;and store the subset of the graph in the database.
 18. Thenon-transitory computer-readable medium of claim 16, wherein the one ormore instructions, when executed by the one or more processors, furthercause the device to: obtain one or more data sets, of the plurality ofdata sets, via an automated web-based interaction, wherein the one ormore data sets indicate information associated with online channels. 19.The non-transitory computer-readable medium of claim 16, wherein the oneor more instructions, that cause the device to process the informationbased on the one or more features to identify the candidate data sets,further cause the device to: compare the plurality of data sets based onthe one or more features; determine that two or more data sets, of theplurality of data sets, have a likelihood of being associated with thesame entity based on comparing the plurality of data sets; and determinethat the two or more data sets are to be included in the candidate datasets based on determining that the two or more data sets have alikelihood of being associated with the same entity.
 20. Thenon-transitory computer-readable medium of claim 16, wherein the one ormore instructions, when executed by the one or more processors, furthercause the device to: provide the one or more temporary credentials.